Overview

Dataset statistics

Number of variables14
Number of observations142
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.7 KiB
Average record size in memory112.9 B

Variable types

Numeric13
Categorical1

Alerts

alcohol is highly overall correlated with color_intensity and 2 other fieldsHigh correlation
malic_acid is highly overall correlated with hueHigh correlation
magnesium is highly overall correlated with prolineHigh correlation
total_phenols is highly overall correlated with flavanoids and 3 other fieldsHigh correlation
flavanoids is highly overall correlated with total_phenols and 5 other fieldsHigh correlation
nonflavanoid_phenols is highly overall correlated with flavanoids and 1 other fieldsHigh correlation
proanthocyanins is highly overall correlated with total_phenols and 2 other fieldsHigh correlation
color_intensity is highly overall correlated with alcohol and 1 other fieldsHigh correlation
hue is highly overall correlated with malic_acid and 2 other fieldsHigh correlation
od280/od315_of_diluted_wines is highly overall correlated with total_phenols and 4 other fieldsHigh correlation
proline is highly overall correlated with alcohol and 2 other fieldsHigh correlation
target is highly overall correlated with alcohol and 6 other fieldsHigh correlation

Reproduction

Analysis started2022-12-27 09:55:43.893673
Analysis finished2022-12-27 09:56:24.162359
Duration40.27 seconds
Software versionpandas-profiling vv3.6.1
Download configurationconfig.json

Variables

alcohol
Real number (ℝ)

Distinct104
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1133504 × 10-15
Minimum-2.4277893
Maximum2.3240519
Zeros0
Zeros (%)0.0%
Negative70
Negative (%)49.3%
Memory size1.2 KiB
2022-12-27T10:56:24.425727image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-2.4277893
5-th percentile-1.6318559
Q1-0.75214001
median0.048170072
Q30.73593655
95-th percentile1.5731359
Maximum2.3240519
Range4.7518411
Interquartile range (IQR)1.4880766

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)9.0136926 × 1014
Kurtosis-0.68486222
Mean1.1133504 × 10-15
Median Absolute Deviation (MAD)0.79405766
Skewness7.8068811 × 10-6
Sum1.5809576 × 10-13
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:24.682379image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.09818945265 6
 
4.2%
-0.7521400136 5
 
3.5%
-1.114780521 4
 
2.8%
-1.214819282 3
 
2.1%
-0.9021981547 3
 
2.1%
-0.8521787743 3
 
2.1%
-0.3144704354 2
 
1.4%
0.6484026367 2
 
1.4%
-0.1519074492 2
 
1.4%
0.5358590308 2
 
1.4%
Other values (94) 110
77.5%
ValueCountFrequency (%)
-2.427789256 1
0.7%
-1.952605142 1
0.7%
-1.902585762 1
0.7%
-1.890080917 1
0.7%
-1.70250824 1
0.7%
-1.664993705 1
0.7%
-1.65248886 1
0.7%
-1.639984015 1
0.7%
-1.477421029 1
0.7%
-1.452411339 1
0.7%
ValueCountFrequency (%)
2.324051879 1
0.7%
2.224013118 1
0.7%
1.773838695 1
0.7%
1.76133385 1
0.7%
1.748829005 1
0.7%
1.711314469 1
0.7%
1.661295089 1
0.7%
1.573761173 1
0.7%
1.561256328 2
1.4%
1.548751483 1
0.7%

malic_acid
Real number (ℝ)

Distinct107
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.8224684 × 10-16
Minimum-1.4579913
Maximum3.1529061
Zeros0
Zeros (%)0.0%
Negative87
Negative (%)61.3%
Memory size1.2 KiB
2022-12-27T10:56:24.914947image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-1.4579913
5-th percentile-1.2256239
Q1-0.66520852
median-0.48295961
Q30.75633297
95-th percentile1.8384359
Maximum3.1529061
Range4.6108974
Interquartile range (IQR)1.4215415

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)-3.5555397 × 1015
Kurtosis0.019151752
Mean-2.8224684 × 10-16
Median Absolute Deviation (MAD)0.43284116
Skewness0.93201269
Sum-3.8191672 × 10-14
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:25.153358image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5558591737 7
 
4.9%
-0.4829596099 4
 
2.8%
-0.6105338465 4
 
2.8%
-0.601421401 3
 
2.1%
-0.7381080831 3
 
2.1%
-0.756332974 3
 
2.1%
-0.6652085193 3
 
2.1%
-0.9021321015 2
 
1.4%
-0.6196462919 2
 
1.4%
-1.102605902 2
 
1.4%
Other values (97) 109
76.8%
ValueCountFrequency (%)
-1.457991275 1
0.7%
-1.321304593 1
0.7%
-1.312192148 1
0.7%
-1.293967257 1
0.7%
-1.275742366 2
1.4%
-1.239292584 1
0.7%
-1.230180138 1
0.7%
-1.139055684 1
0.7%
-1.102605902 2
1.4%
-1.06615612 1
0.7%
ValueCountFrequency (%)
3.152906133 1
0.7%
2.888645214 1
0.7%
2.460360277 1
0.7%
2.168762022 1
0.7%
2.068525122 1
0.7%
2.059412676 1
0.7%
1.904501103 1
0.7%
1.840713985 1
0.7%
1.795151758 1
0.7%
1.767814421 1
0.7%

ash
Real number (ℝ)

Distinct70
Distinct (%)49.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6323575 × 10-16
Minimum-3.7574149
Maximum3.1975814
Zeros0
Zeros (%)0.0%
Negative74
Negative (%)52.1%
Memory size1.2 KiB
2022-12-27T10:56:26.623961image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-3.7574149
5-th percentile-1.6578286
Q1-0.61644882
median-0.018169572
Q30.72967949
95-th percentile1.3634816
Maximum3.1975814
Range6.9549962
Interquartile range (IQR)1.3461283

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)1.0418424 × 1015
Kurtosis1.0214061
Mean9.6323575 × 10-16
Median Absolute Deviation (MAD)0.6356717
Skewness-0.29422507
Sum1.3855583 × 10-13
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:27.122255image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.253173827 6
 
4.2%
-0.01816957219 5
 
3.5%
-0.6164488187 5
 
3.5%
-0.2425242896 5
 
3.5%
0.9540342034 4
 
2.8%
0.4305398627 4
 
2.8%
-0.3173091955 4
 
2.8%
-0.1677393838 4
 
2.8%
-1.439082783 3
 
2.1%
1.028819109 3
 
2.1%
Other values (60) 99
69.7%
ValueCountFrequency (%)
-3.757414863 1
 
0.7%
-2.448679011 1
 
0.7%
-2.2991092 1
 
0.7%
-2.037362029 1
 
0.7%
-1.813007312 1
 
0.7%
-1.738222406 1
 
0.7%
-1.6634375 2
1.4%
-1.551260141 1
 
0.7%
-1.439082783 3
2.1%
-1.40169033 1
 
0.7%
ValueCountFrequency (%)
3.197581378 1
 
0.7%
1.888845526 1
 
0.7%
1.851453073 1
 
0.7%
1.776668167 1
 
0.7%
1.55231345 1
 
0.7%
1.440136091 1
 
0.7%
1.402743638 1
 
0.7%
1.365351185 1
 
0.7%
1.327958733 2
 
1.4%
1.253173827 6
4.2%

alcalinity_of_ash
Real number (ℝ)

Distinct56
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4416565 × 10-16
Minimum-2.6790026
Maximum3.0795151
Zeros0
Zeros (%)0.0%
Negative73
Negative (%)51.4%
Memory size1.2 KiB
2022-12-27T10:56:27.831959image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-2.6790026
5-th percentile-1.488711
Q1-0.71991923
median-0.037208369
Q30.55645325
95-th percentile1.5953611
Maximum3.0795151
Range5.7585177
Interquartile range (IQR)1.2763725

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)1.8441808 × 1015
Kurtosis0.45246477
Mean5.4416565 × 10-16
Median Absolute Deviation (MAD)0.60850316
Skewness0.10757147
Sum7.9936058 × 10-14
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:28.941683image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1112070357 12
 
8.5%
0.4080378454 10
 
7.0%
-1.076116203 9
 
6.3%
0.5564532503 8
 
5.6%
-0.0372083691 7
 
4.9%
0.8532840599 7
 
4.9%
-0.1856237739 7
 
4.9%
-0.3340391788 6
 
4.2%
-0.8386515552 5
 
3.5%
-0.4824545836 5
 
3.5%
Other values (46) 66
46.5%
ValueCountFrequency (%)
-2.679002575 1
0.7%
-2.500904089 1
0.7%
-2.441537927 1
0.7%
-2.144707118 1
0.7%
-1.90724247 1
0.7%
-1.669777822 2
1.4%
-1.491679337 1
0.7%
-1.432313175 1
0.7%
-1.313580851 2
1.4%
-1.224531608 1
0.7%
ValueCountFrequency (%)
3.079515133 1
 
0.7%
2.634268918 1
 
0.7%
2.189022703 1
 
0.7%
2.040607299 1
 
0.7%
1.892191894 1
 
0.7%
1.743776489 1
 
0.7%
1.595361084 5
3.5%
1.446945679 2
 
1.4%
1.298530274 4
2.8%
1.179797951 1
 
0.7%

magnesium
Real number (ℝ)

Distinct49
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2509555 × 10-17
Minimum-1.9908823
Maximum4.1899367
Zeros0
Zeros (%)0.0%
Negative78
Negative (%)54.9%
Memory size1.2 KiB
2022-12-27T10:56:29.811948image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-1.9908823
5-th percentile-1.2485122
Q1-0.78159163
median-0.17694629
Q30.54526898
95-th percentile1.8284608
Maximum4.1899367
Range6.1808191
Interquartile range (IQR)1.3268606

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)8.0221864 × 1016
Kurtosis2.1664001
Mean1.2509555 × 10-17
Median Absolute Deviation (MAD)0.60464534
Skewness1.1671264
Sum5.3290705 × 10-15
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:31.047529image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-0.7815916321 11
 
7.7%
-0.9159572637 11
 
7.7%
-0.2441291054 7
 
4.9%
0.8307959479 6
 
4.2%
0.1589677896 6
 
4.2%
0.09178497374 6
 
4.2%
-0.9831400796 5
 
3.5%
-0.1097634738 5
 
3.5%
-0.7144088163 5
 
3.5%
-1.319054159 4
 
2.8%
Other values (39) 76
53.5%
ValueCountFrequency (%)
-1.990882317 1
 
0.7%
-1.45341979 2
 
1.4%
-1.319054159 4
 
2.8%
-1.251871343 1
 
0.7%
-1.184688527 1
 
0.7%
-1.050322895 3
 
2.1%
-0.9831400796 5
3.5%
-0.9159572637 11
7.7%
-0.8487744479 3
 
2.1%
-0.7815916321 11
7.7%
ValueCountFrequency (%)
4.18993674 1
0.7%
3.450925765 1
0.7%
2.644731975 1
0.7%
2.443183528 1
0.7%
2.308817896 1
0.7%
2.174452265 1
0.7%
1.905721001 1
0.7%
1.838538185 1
0.7%
1.636989738 1
0.7%
1.569806922 1
0.7%

total_phenols
Real number (ℝ)

Distinct82
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.5331849 × 10-16
Minimum-2.0891521
Maximum2.5938419
Zeros0
Zeros (%)0.0%
Negative69
Negative (%)48.6%
Memory size1.2 KiB
2022-12-27T10:56:31.881648image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-2.0891521
5-th percentile-1.4432219
Q1-0.91840358
median0.098936493
Q30.84983036
95-th percentile1.4941457
Maximum2.5938419
Range4.682994
Interquartile range (IQR)1.7682339

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)-3.9615735 × 1015
Kurtosis-0.77530339
Mean-2.5331849 × 10-16
Median Absolute Deviation (MAD)0.83163514
Skewness0.08710311
Sum-3.4638958 × 10-14
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:32.797036image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1190649517 8
 
5.6%
0.849830357 6
 
4.2%
0.5268652541 5
 
3.5%
1.17279546 5
 
3.5%
0.9305716328 4
 
2.8%
-1.443221874 4
 
2.8%
0.2361966615 3
 
2.1%
-1.281739322 3
 
2.1%
1.092054184 3
 
2.1%
-0.4743265649 3
 
2.1%
Other values (72) 98
69.0%
ValueCountFrequency (%)
-2.089152079 1
 
0.7%
-1.895373018 1
 
0.7%
-1.65314919 1
 
0.7%
-1.604704425 1
 
0.7%
-1.572407915 1
 
0.7%
-1.443221874 4
2.8%
-1.427073618 2
1.4%
-1.410925363 2
1.4%
-1.394777108 1
 
0.7%
-1.330184088 2
1.4%
ValueCountFrequency (%)
2.593841913 1
0.7%
2.545397147 1
0.7%
1.980208217 1
0.7%
1.786429155 1
0.7%
1.657243114 1
0.7%
1.608798349 1
0.7%
1.576501839 1
0.7%
1.495760563 1
0.7%
1.463464053 1
0.7%
1.415019287 1
0.7%

flavanoids
Real number (ℝ)

Distinct110
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0257487 × 10-16
Minimum-1.7345167
Maximum1.8497355
Zeros0
Zeros (%)0.0%
Negative65
Negative (%)45.8%
Memory size1.2 KiB
2022-12-27T10:56:33.364882image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7345167
5-th percentile-1.554777
Q1-0.7989215
median0.15775758
Q30.83244035
95-th percentile1.3742949
Maximum1.8497355
Range3.5842522
Interquartile range (IQR)1.6313618

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)3.3166662 × 1015
Kurtosis-1.2569624
Mean3.0257487 × 10-16
Median Absolute Deviation (MAD)0.8380825
Skewness-0.15692077
Sum3.8191672 × 10-14
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:33.932577image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7006663674 4
 
2.8%
-1.460426868 3
 
2.1%
0.732292122 3
 
2.1%
-1.397175359 2
 
1.4%
1.480768316 2
 
1.4%
-1.355007686 2
 
1.4%
0.7428340402 2
 
1.4%
0.4687441665 2
 
1.4%
-1.217962749 2
 
1.4%
0.5741633487 2
 
1.4%
Other values (100) 118
83.1%
ValueCountFrequency (%)
-1.734516742 1
0.7%
-1.597471805 2
1.4%
-1.586929886 1
0.7%
-1.576387968 1
0.7%
-1.56584605 2
1.4%
-1.555304132 1
0.7%
-1.544762214 1
0.7%
-1.513136459 1
0.7%
-1.502594541 1
0.7%
-1.492052622 1
0.7%
ValueCountFrequency (%)
1.849735453 1
0.7%
1.797025862 1
0.7%
1.638897089 1
0.7%
1.586187498 1
0.7%
1.491310234 1
0.7%
1.480768316 2
1.4%
1.375349133 1
0.7%
1.354265297 1
0.7%
1.333181461 1
0.7%
1.322639542 1
0.7%

nonflavanoid_phenols
Real number (ℝ)

Distinct36
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.7144515 × 10-16
Minimum-1.937363
Maximum2.2717181
Zeros0
Zeros (%)0.0%
Negative76
Negative (%)53.5%
Memory size1.2 KiB
2022-12-27T10:56:34.599855image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-1.937363
5-th percentile-1.3480916
Q1-0.75882026
median-0.1695489
Q30.5880857
95-th percentile1.85081
Maximum2.2717181
Range4.2090811
Interquartile range (IQR)1.346906

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)-1.0330381 × 1015
Kurtosis-0.60636618
Mean-9.7144515 × 10-16
Median Absolute Deviation (MAD)0.67345298
Skewness0.39112149
Sum-1.3855583 × 10-13
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:35.341964image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.5880856999 10
 
7.0%
-0.5904570132 9
 
6.3%
-0.8430018803 9
 
6.3%
-0.5062753909 8
 
5.6%
0.3355408328 8
 
5.6%
0.08299596571 7
 
4.9%
-0.1695489014 7
 
4.9%
-0.3379121461 6
 
4.2%
-0.758820258 6
 
4.2%
1.429901924 6
 
4.2%
Other values (26) 66
46.5%
ValueCountFrequency (%)
-1.937362971 1
 
0.7%
-1.853181349 2
 
1.4%
-1.600636482 3
 
2.1%
-1.432273237 1
 
0.7%
-1.348091615 2
 
1.4%
-1.263909992 5
3.5%
-1.17972837 4
2.8%
-1.011365125 5
3.5%
-0.9271835027 1
 
0.7%
-0.8430018803 9
6.3%
ValueCountFrequency (%)
2.271718147 3
2.1%
2.103354903 2
 
1.4%
2.01917328 1
 
0.7%
1.850810035 3
2.1%
1.598265168 1
 
0.7%
1.429901924 6
4.2%
1.345720301 4
2.8%
1.177357056 5
3.5%
1.008993812 4
2.8%
0.9248121894 2
 
1.4%

proanthocyanins
Real number (ℝ)

Distinct87
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.0914413 × 10-16
Minimum-2.0428363
Maximum3.4268308
Zeros0
Zeros (%)0.0%
Negative73
Negative (%)51.4%
Memory size1.2 KiB
2022-12-27T10:56:36.022943image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-2.0428363
5-th percentile-1.5045241
Q1-0.60618325
median-0.069601977
Q30.60545188
95-th percentile2.0022941
Maximum3.4268308
Range5.4696671
Interquartile range (IQR)1.2116351

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)-4.7983171 × 1015
Kurtosis0.67735441
Mean-2.0914413 × 10-16
Median Absolute Deviation (MAD)0.60581756
Skewness0.55177035
Sum-2.7533531 × 10-14
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:41.926632image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4330925141 6
 
4.2%
0.4669792911 5
 
3.5%
-0.2426927091 5
 
3.5%
0.1034887544 4
 
2.8%
-0.06960197735 4
 
2.8%
0.6573790961 4
 
2.8%
0.8304698279 3
 
2.1%
-0.7965830508 3
 
2.1%
0.05156153489 3
 
2.1%
0.03425246171 3
 
2.1%
Other values (77) 102
71.8%
ValueCountFrequency (%)
-2.04283632 2
1.4%
-1.817818368 1
0.7%
-1.696654856 1
0.7%
-1.66203671 2
1.4%
-1.592800417 1
0.7%
-1.506255051 1
0.7%
-1.471636905 1
0.7%
-1.385091539 2
1.4%
-1.367782466 1
0.7%
-1.333164319 2
1.4%
ValueCountFrequency (%)
3.426830804 1
 
0.7%
2.907558609 1
 
0.7%
2.267122902 2
1.4%
2.09403217 3
2.1%
2.007486804 1
 
0.7%
1.903632365 1
 
0.7%
1.557450901 1
 
0.7%
1.540141828 1
 
0.7%
1.470905535 1
 
0.7%
1.349742023 1
 
0.7%

color_intensity
Real number (ℝ)

Distinct112
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6910832 × 10-16
Minimum-1.5943742
Maximum3.4427793
Zeros0
Zeros (%)0.0%
Negative78
Negative (%)54.9%
Memory size1.2 KiB
2022-12-27T10:56:42.417098image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-1.5943742
5-th percentile-1.2587073
Q1-0.83042026
median-0.1502756
Q30.51267741
95-th percentile2.0180213
Maximum3.4427793
Range5.0371535
Interquartile range (IQR)1.3430977

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)2.1392497 × 1015
Kurtosis0.61036447
Mean4.6910832 × 10-16
Median Absolute Deviation (MAD)0.67692123
Skewness0.92390597
Sum6.6613381 × 10-14
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:42.786140image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1674672519 4
 
2.8%
-0.6832167592 3
 
2.1%
-1.027049764 3
 
2.1%
-0.8336436988 3
 
2.1%
0.176365753 3
 
2.1%
-0.8981123872 3
 
2.1%
0.04742837614 3
 
2.1%
0.004449250537 3
 
2.1%
-1.091518452 2
 
1.4%
-1.005560201 2
 
1.4%
Other values (102) 113
79.6%
ValueCountFrequency (%)
-1.594374222 1
0.7%
-1.396670244 1
0.7%
-1.327903643 1
0.7%
-1.30641408 2
1.4%
-1.284924518 1
0.7%
-1.259137042 2
1.4%
-1.250541217 1
0.7%
-1.233349567 1
0.7%
-1.198966266 1
0.7%
-1.155987141 1
0.7%
ValueCountFrequency (%)
3.442779299 1
0.7%
2.905540229 1
0.7%
2.497238536 1
0.7%
2.376896984 1
0.7%
2.265151257 1
0.7%
2.239363782 1
0.7%
2.110425976 1
0.7%
2.024468154 1
0.7%
1.895530777 1
0.7%
1.727912187 1
0.7%

hue
Real number (ℝ)

Distinct69
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.0694275 × 10-16
Minimum-2.0765626
Maximum3.3695967
Zeros0
Zeros (%)0.0%
Negative64
Negative (%)45.1%
Memory size1.2 KiB
2022-12-27T10:56:43.078221image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-2.0765626
5-th percentile-1.6780632
Q1-0.74823107
median0.048767853
Q30.62437819
95-th percentile1.5918463
Maximum3.3695967
Range5.4461593
Interquartile range (IQR)1.3726093

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)-1.1065085 × 1015
Kurtosis-0.075416638
Mean-9.0694275 × 10-16
Median Absolute Deviation (MAD)0.70844349
Skewness0.063962524
Sum-1.3322676 × 10-13
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:43.382742image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.402989597 7
 
4.9%
-1.67806315 5
 
3.5%
0.04876785281 5
 
3.5%
0.7572113413 5
 
3.5%
1.067155367 4
 
2.8%
0.4472673151 4
 
2.8%
1.24426624 4
 
2.8%
-0.2611761734 4
 
2.8%
0.6243781872 3
 
2.1%
0.1373232889 3
 
2.1%
Other values (59) 98
69.0%
ValueCountFrequency (%)
-2.076562613 1
 
0.7%
-1.810896304 1
 
0.7%
-1.766618586 1
 
0.7%
-1.722340868 2
 
1.4%
-1.67806315 5
3.5%
-1.633785432 2
 
1.4%
-1.589507714 2
 
1.4%
-1.545229996 1
 
0.7%
-1.500952278 1
 
0.7%
-1.45667456 1
 
0.7%
ValueCountFrequency (%)
3.369596705 1
 
0.7%
2.085542882 1
 
0.7%
1.90843201 1
 
0.7%
1.819876574 2
1.4%
1.68704342 1
 
0.7%
1.598487984 2
1.4%
1.46565483 2
1.4%
1.332821676 2
1.4%
1.24426624 4
2.8%
1.199988522 1
 
0.7%
Distinct104
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.013274 × 10-15
Minimum-1.9443599
Maximum1.9117728
Zeros0
Zeros (%)0.0%
Negative59
Negative (%)41.5%
Memory size1.2 KiB
2022-12-27T10:56:43.697009image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-1.9443599
5-th percentile-1.6504207
Q1-0.92575877
median0.2529082
Q30.80586307
95-th percentile1.3719142
Maximum1.9117728
Range3.8561327
Interquartile range (IQR)1.7316218

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)9.9039338 × 1014
Kurtosis-1.0697532
Mean1.013274 × 10-15
Median Absolute Deviation (MAD)0.73484793
Skewness-0.32935573
Sum1.4566126 × 10-13
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:44.071699image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2529081972 4
 
2.8%
-1.144030435 4
 
2.8%
0.383871194 4
 
2.8%
1.053237622 3
 
2.1%
-1.245890544 3
 
2.1%
-1.857051195 3
 
2.1%
0.5730399671 3
 
2.1%
-0.4310096748 3
 
2.1%
-1.493265093 2
 
1.4%
-1.522367981 2
 
1.4%
Other values (94) 111
78.2%
ValueCountFrequency (%)
-1.94435986 1
 
0.7%
-1.915256971 1
 
0.7%
-1.900705527 1
 
0.7%
-1.857051195 3
2.1%
-1.726088198 1
 
0.7%
-1.653330978 1
 
0.7%
-1.595125202 1
 
0.7%
-1.536919425 1
 
0.7%
-1.522367981 2
1.4%
-1.493265093 2
1.4%
ValueCountFrequency (%)
1.911772823 1
0.7%
1.766258382 1
0.7%
1.504332389 1
0.7%
1.489780944 1
0.7%
1.431575168 1
0.7%
1.417023724 1
0.7%
1.40247228 1
0.7%
1.373369392 1
0.7%
1.344266504 1
0.7%
1.329715059 1
0.7%

proline
Real number (ℝ)

Distinct100
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.5558759 × 10-16
Minimum-1.536033
Maximum2.581323
Zeros0
Zeros (%)0.0%
Negative84
Negative (%)59.2%
Memory size1.2 KiB
2022-12-27T10:56:44.424323image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-1.536033
5-th percentile-1.20285
Q1-0.8095876
median-0.21961076
Q30.80473549
95-th percentile1.8157677
Maximum2.581323
Range4.117356
Interquartile range (IQR)1.6143231

Descriptive statistics

Standard deviation1.0035398
Coefficient of variation (CV)-6.4499991 × 1015
Kurtosis-0.54795879
Mean-1.5558759 × 10-16
Median Absolute Deviation (MAD)0.6740215
Skewness0.68654213
Sum-2.1316282 × 10-14
Variance1.0070922
MonotonicityNot monotonic
2022-12-27T10:56:44.806542image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7305356954 5
 
3.5%
-0.1979755016 4
 
2.8%
0.03501958317 4
 
2.8%
-0.3810430683 4
 
2.8%
-0.5907386446 3
 
2.1%
-0.7638207076 3
 
2.1%
-0.8137482257 3
 
2.1%
-0.8636757439 3
 
2.1%
-0.9635307802 3
 
2.1%
-1.196525865 2
 
1.4%
Other values (90) 108
76.1%
ValueCountFrequency (%)
-1.536032989 1
0.7%
-1.379593432 1
0.7%
-1.323008911 1
0.7%
-1.313023407 1
0.7%
-1.289723899 1
0.7%
-1.246453383 1
0.7%
-1.223153875 1
0.7%
-1.203182867 1
0.7%
-1.196525865 2
1.4%
-1.179883359 1
0.7%
ValueCountFrequency (%)
2.58132301 1
0.7%
2.464825467 1
0.7%
2.364970431 1
0.7%
2.11533284 1
0.7%
1.932265274 1
0.7%
1.898980262 1
0.7%
1.832410237 1
0.7%
1.815767731 2
1.4%
1.799125225 2
1.4%
1.765840213 1
0.7%

target
Categorical

Distinct3
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1
57 
0
47 
2
38 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters142
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
1 57
40.1%
0 47
33.1%
2 38
26.8%

Length

2022-12-27T10:56:45.639096image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-27T10:56:46.337425image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
1 57
40.1%
0 47
33.1%
2 38
26.8%

Most occurring characters

ValueCountFrequency (%)
1 57
40.1%
0 47
33.1%
2 38
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 142
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 57
40.1%
0 47
33.1%
2 38
26.8%

Most occurring scripts

ValueCountFrequency (%)
Common 142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 57
40.1%
0 47
33.1%
2 38
26.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 57
40.1%
0 47
33.1%
2 38
26.8%

Interactions

2022-12-27T10:56:19.961484image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:44.866587image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:48.650401image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:52.645944image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:55.142687image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:57.881385image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:00.526185image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:03.119306image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:06.220415image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:08.936968image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:11.741089image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:14.304016image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:17.391363image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:20.111830image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:45.119127image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:50.177413image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:52.834598image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:55.311584image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:58.056202image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:00.719817image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:03.346432image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:06.391658image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:09.296387image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:11.899899image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:14.516773image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:17.580463image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:20.293755image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:45.315518image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:50.474446image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:53.006022image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:55.486531image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:58.295764image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:00.931950image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:03.747384image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:06.545653image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:09.541963image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:12.125289image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:14.734959image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:17.771381image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:20.493491image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:45.561682image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:50.689903image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:53.230128image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:55.695247image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:58.518882image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:01.175431image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:03.972302image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:06.720633image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:09.725806image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:12.321860image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:14.979315image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:18.002329image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:20.699877image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:45.830450image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:50.878499image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:53.427480image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:55.872044image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:58.708475image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:01.409903image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:04.183466image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:06.883179image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:09.941341image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:12.498669image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:15.133851image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:18.197869image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:20.901560image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:46.023719image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:51.101862image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:53.641125image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:56.074621image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:58.897419image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:01.581181image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:04.434253image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:07.069842image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:10.136846image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:12.713249image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:15.356326image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:18.416517image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:21.099815image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:46.226595image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:51.328844image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:53.814771image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:56.255713image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:59.084652image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:01.751326image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:04.624660image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:07.241032image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:10.356434image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:12.892698image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:15.542557image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:18.636109image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:21.287036image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:46.719802image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:51.612502image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:54.013108image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:56.441090image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:59.296504image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:01.954793image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:04.808124image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:07.449576image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:10.598325image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:13.141548image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:15.944072image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:18.824773image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:21.475344image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:47.023625image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:51.770133image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:54.192615image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:56.800679image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:59.495628image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:02.135410image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:04.986518image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:07.658441image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:10.792610image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:13.333512image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:16.119530image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:19.008079image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:21.655992image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:47.277756image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:51.928388image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:54.389012image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:56.990570image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:59.679876image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:02.366438image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:05.208398image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:07.853807image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:10.991257image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:13.534146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:16.426827image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:19.197865image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:21.814438image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:47.824315image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:52.099213image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:54.560072image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:57.172693image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:59.852364image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:02.572443image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:05.370984image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:07.997765image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:11.161341image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:13.726594image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:16.886266image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:19.377945image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:21.978642image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:48.058628image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:52.273618image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:54.741330image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:57.361579image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:00.028792image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:02.740651image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:05.798773image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:08.162269image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:11.324763image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:13.911955image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:17.046009image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:19.574930image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:22.164766image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:48.479067image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:52.483226image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:54.964922image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:55:57.642663image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:00.297155image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:02.942714image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:06.039738image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:08.656755image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:11.533883image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:14.152706image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:17.246137image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-27T10:56:19.773528image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2022-12-27T10:56:46.617925image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetarget
alcohol1.0000.1690.259-0.2970.3940.3320.311-0.1530.1660.637-0.0360.1030.6040.560
malic_acid0.1691.0000.1890.2020.111-0.250-0.2900.215-0.2420.312-0.593-0.282-0.0380.491
ash0.2590.1891.0000.3780.3390.1530.0820.1250.0390.268-0.033-0.0290.2610.174
alcalinity_of_ash-0.2970.2020.3781.000-0.178-0.320-0.4100.340-0.235-0.117-0.279-0.313-0.4480.380
magnesium0.3940.1110.339-0.1781.0000.2640.227-0.2660.2050.3740.0050.0340.5340.426
total_phenols0.332-0.2500.153-0.3200.2641.0000.875-0.4180.6620.0060.4220.6840.4310.556
flavanoids0.311-0.2900.082-0.4100.2270.8751.000-0.5150.726-0.0540.5260.7390.4330.724
nonflavanoid_phenols-0.1530.2150.1250.340-0.266-0.418-0.5151.000-0.3630.049-0.242-0.511-0.2640.309
proanthocyanins0.166-0.2420.039-0.2350.2050.6620.726-0.3631.000-0.0830.3690.5470.2960.396
color_intensity0.6370.3120.268-0.1170.3740.006-0.0540.049-0.0831.000-0.440-0.3270.4420.657
hue-0.036-0.593-0.033-0.2790.0050.4220.526-0.2420.369-0.4401.0000.4940.2090.580
od280/od315_of_diluted_wines0.103-0.282-0.029-0.3130.0340.6840.739-0.5110.547-0.3270.4941.0000.2860.623
proline0.604-0.0380.261-0.4480.5340.4310.433-0.2640.2960.4420.2090.2861.0000.632
target0.5600.4910.1740.3800.4260.5560.7240.3090.3960.6570.5800.6230.6321.000

Missing values

2022-12-27T10:56:22.956309image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-27T10:56:23.905975image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetarget
00.385801-0.6378711.776668-1.2245320.6964300.5268650.732292-0.169549-0.415783-0.1674670.6243780.2529080.4677250
10.948519-0.7654451.2531740.8532840.0917851.1727951.333181-0.5904571.3497420.3053031.0671550.1510481.8157680
20.523354-0.5194090.954034-1.046433-0.4456780.9305721.006382-0.169549-0.260002-0.081509-0.1283430.8931721.5162030
30.973529-0.5558590.168793-1.076116-0.7144090.5268650.816627-0.5904570.3631250.2623240.8900440.4275261.9322650
40.4358200.8201200.0566150.556453-0.512860-0.555068-1.2917560.756449-0.6061831.474335-1.766619-1.435059-0.2978312
5-0.2519460.045562-0.317309-0.037208-0.915957-1.427074-1.5553041.008994-1.6620372.110426-1.678063-1.420508-0.8969612
6-0.952218-1.047931-2.299109-0.8386523.450926-0.684254-0.743576-1.8531811.557451-0.9196021.4656550.674900-0.0714921
70.6609070.7107710.9540341.2985301.569807-1.410925-0.437861-1.179728-0.6061831.551698-1.589508-1.900706-0.7971062
80.998538-0.4009481.178389-0.7496021.0323441.1727950.8482530.2513590.1381070.5631780.8014890.4711802.1153330
92.324052-0.637871-0.728626-1.669778-0.1769460.8498301.048550-0.5904570.6573790.0904080.5801000.3547681.0169270
alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetarget
1320.648403-0.619646-0.4668791.298530-0.848774-0.635809-0.153229-0.758820-0.986983-0.5370880.1373230.252908-0.8903041
1330.7609460.2186991.2157811.4469460.360516-1.168702-1.2074210.251359-0.1042201.577485-0.925342-1.1731330.0350202
134-0.8521790.756333-0.579056-0.482455-0.7815920.9305721.0590920.7564492.094032-1.1559872.0855430.325665-1.1099851
135-0.802159-1.230180-1.551260-1.4323132.443184-0.603513-0.142687-0.0853672.007487-0.6832170.491545-0.4310100.0350201
136-0.5645670.082012-0.7286260.408038-0.7815920.4461240.300073-0.843002-0.658110-1.284925-0.2168980.252908-1.3795931
1370.6859170.7563331.3279591.150115-0.176946-1.168702-1.5447621.177357-1.817818-0.274915-0.261176-0.794796-0.7305362
138-1.652489-0.6105340.9540341.892192-0.781592-0.571216-0.3956930.335541-0.450402-1.0270501.8198770.878620-0.5907391
1390.673412-0.4920721.066212-0.1856240.6964300.1231590.574163-0.590457-0.104220-0.3393840.6686560.3838711.1833530
140-0.0143541.011481-0.055562-0.3340390.427699-1.427074-1.3550080.335541-1.1427650.124791-1.191008-1.245891-0.2146182
141-0.1143930.5923090.1314000.1112070.293333-1.572408-0.806828-1.011365-1.3331640.176366-0.925342-1.726088-0.6972512